{
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "'/workspaces/vscode-dev-containers/containers/jupyter-datascience-notebooks/test-project'"
      ]
     },
     "metadata": {},
     "execution_count": 1
    }
   ],
   "source": [
    "import os\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   height  weight\n",
       "0      30      45\n",
       "1      50      25\n",
       "2      40      25"
      ],
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>height</th>\n      <th>weight</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>30</td>\n      <td>45</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>50</td>\n      <td>25</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>40</td>\n      <td>25</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame( { 'height' : [ 30,50,40 ], 'weight': [ 45, 25, 25 ] } )\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "df.plot()\n",
    "plt.ylim( 0, 50 )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}
